Bridging the C-suite and V-suite: Aligning Generative AI Strategy for CX Transformation

In the era of generative AI, customer experience (CX) is no longer just a competitive differentiator—it’s a strategic imperative. Yet, as organizations race to harness the transformative power of AI, a critical challenge has emerged: aligning the perspectives and priorities of the C-suite (executive leadership) and the V-suite (VPs and functional leaders) to unlock the full value of generative AI for CX transformation.

The C-suite vs. V-suite: Differing Perspectives on Generative AI

Recent research reveals a clear divide in how C-suite and V-suite leaders view the potential of generative AI. The C-suite—CEOs, CFOs, CIOs, and their peers—tend to focus on high-visibility, customer-facing applications such as chatbots, customer service, and sales enablement. For these leaders, generative AI is a tool to accelerate customer acquisition, drive brand loyalty, and deliver hyper-personalized experiences that directly impact the bottom line.

In contrast, the V-suite—VPs of strategy, innovation, customer experience, and data—see broader opportunities. They recognize generative AI’s potential to transform not only the frontstage of customer engagement but also the backstage: operations, IT, HR, finance, and beyond. V-suite leaders are often closer to the day-to-day realities of implementation and are more attuned to the ways AI can drive efficiency, automate processes, and unlock new sources of value across the enterprise.

This divergence is not just about functional focus—it extends to risk perception and innovation appetite. C-suite leaders are more likely to express concern about the risks and ethics of generative AI, with over half citing these as top worries. Meanwhile, less than a quarter of V-suite leaders share the same level of concern, reflecting a greater willingness to experiment and innovate from the ground up.

The Alignment Imperative: Why Bridging the Gap Matters

Without alignment between top-down strategy and bottom-up innovation, organizations risk missing out on the true potential of generative AI. When the C-suite focuses solely on flagship, customer-facing projects, they may overlook transformative use cases in data management, software development, or operational automation—areas where the V-suite often sees untapped value. Conversely, if V-suite experimentation happens in silos, it can lead to shadow IT, duplicated efforts, and increased risk exposure.

The result is a fragmented approach to AI adoption, where innovation energy is dissipated and ROI is difficult to measure. In fact, more than two-thirds of organizations report that they do not yet have a clear way to measure the success of their generative AI initiatives, underscoring the need for a unified, enterprise-wide strategy.

Common Gaps and How to Address Them

• 1. Risk Perception and Governance
  
  • The C-suite’s heightened concern about risk can lead to overly cautious policies that stifle innovation. Meanwhile, the V-suite’s appetite for experimentation can expose the organization to reputational, regulatory, and data security risks if not properly governed.
  • Solution: Establish robust governance frameworks that balance risk management with innovation. Foster open communication between the CIO’s office, risk management, and business units to ensure that experimentation is encouraged—but within clear guardrails.
• 2. Functional Focus and Portfolio Balance
  
  • C-suite leaders often prioritize customer-facing AI, while the V-suite sees value in back-office transformation. This can result in underinvestment in areas like operations, HR, and finance, where generative AI can drive significant efficiency gains.
  • Solution: Adopt a portfolio approach to AI investment. Balance flagship, high-visibility projects with a pipeline of smaller, domain-driven initiatives. Empower domain experts to identify and champion use cases, while ensuring alignment with enterprise strategy.
• 3. Measurement and Maturity
  
  • There is widespread uncertainty about what generative AI maturity looks like and how to measure success. Organizations at different stages of adoption often engage in similar activities, making it difficult to benchmark progress or justify further investment.
  • Solution: Develop clear, outcome-based metrics for AI initiatives. Move beyond activity-based measures (e.g., number of pilots) to focus on business impact—such as cost savings, customer satisfaction, speed to market, and employee productivity.

Fostering Collaboration: Practical Guidance for Leaders

1. Create Cross-Functional AI Councils: Bring together C-suite and V-suite leaders, along with practitioners, to share insights, surface emerging use cases, and align on priorities. Use these forums to identify both quick wins and long-term bets.
2. Encourage Bottom-Up Innovation—With Guardrails: Recognize that some of the most impactful AI use cases will emerge from the front lines. Provide resources, training, and safe sandboxes for experimentation, but ensure that all initiatives are visible to central IT and risk functions.
3. Upskill and Empower Teams: Invest in AI literacy and change management at all levels. Equip employees to identify opportunities, manage risks, and adapt to new ways of working as AI becomes embedded in daily operations.
4. Communicate Early and Often: Transparency is key. Regularly update the organization on AI strategy, progress, and lessons learned. Celebrate both successes and failures as part of a culture of continuous improvement.

Building a Balanced Portfolio of AI Initiatives

To maximize the value of generative AI, organizations should avoid the trap of “zero-risk, zero-innovation.” Instead, leaders should:

• Focus on projects that are delivering measurable value
• Control shadow IT and avoid duplication of effort
• Connect business and technology teams to ensure alignment
• Engage risk and compliance functions early in the process
• Empower domain experts and early adopters to drive innovation

Measuring Success: From Experimentation to Enterprise Value

Success in generative AI is not about the number of pilots or the latest technology deployed—it’s about delivering tangible business outcomes. Leading organizations measure:

• Customer impact: Increases in satisfaction, loyalty, and lifetime value
• Operational efficiency: Cost savings, process automation, and speed to market
• Employee empowerment: Productivity gains, reduced manual work, and improved decision-making
• Risk management: Compliance, data security, and ethical AI adoption

The Publicis Sapient Approach: Your Partner for Enterprise-Wide AI Transformation

At Publicis Sapient, we understand that bridging the C-suite and V-suite is essential to unlocking the full value of generative AI. Our SPEED framework—Strategy, Product, Experience, Engineering, and Data & AI—ensures that every AI initiative is grounded in business objectives, powered by cross-functional collaboration, and measured by real-world outcomes.

Whether you’re just beginning your AI journey or seeking to scale proven solutions, we help organizations:

• Align leadership around a shared vision for AI-driven CX transformation
• Build robust data and governance foundations
• Foster a culture of innovation and continuous learning
• Deliver measurable value—at speed and at scale

Ready to bridge the gap and accelerate your generative AI transformation? Connect with Publicis Sapient’s experts to shape the future of customer experience—together.